Emotion in Brain-Inspired Developmental Networks
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Transcript of Emotion in Brain-Inspired Developmental Networks
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Emotion in Brain-Inspired Developmental Networks
Juyang (John) WengComputer Sci., Neurosci., Cognitive Sci.
Michigan State UniversityEast Lansing, MI 49924 USA
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Patterns of Communication(a) Point to Point(b) Release hormones into blood
(c) Autonomic nervous system;(d) Diffuse modulatory systems
Beart, Connors & Paradiso 2007
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Major Neurotransmitters
Beart, Connors & Paradiso 2007
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BMI 831Cognitive Science
for Brain-Mind ResearchLecturer:
Juyang Weng
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BMI 871Introduction to Computational
Brain-Mind Lecturer:
Juyang Weng
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Motivation/emotion in the Brain Motivational system in the brain:
biological causality for the development of emotion Lower (physiology) emotion:
Arousal: noradrenaline, oxytocin, cortisol Habituation sensitization, familiarization
(acetylcholine and norepinephrine systems) Pain avoidance (serotonin system) Pleasure seeking (dopamine system)
Basic emotion: angry, happy, sad, scared, disgust, etc. Higher emotion: moods, dispositions (e.g., jealousy)
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Neuromodulatory Systems
Jeff Krichmar, AB, 2008
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Quiz: Why Modulations
Quiz: The main purpose of modulatory systems is:A. Animal likeB. Establish bounds with
humanC. Look intelligentD. Speed-up learningE. Being quick to fight or
flee
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Quiz: Why Modulations
Quiz: The main purpose of modulatory systems is:A. Animal likeB. Establish bounds with
humanC. Look intelligentD. Speed-up learningE. Being quick to fight or
flee
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Why Motivation? Evolution: Pressure of survival Understanding of the environment should not be
the primary goal of a life Fruit flies, Rats, Humans
Sexual selection: survival is not sufficient Males: Fight with males, court with females Females: Select the male winners
Motivation: quick ways to sense a variety of values Neuromodulators, diffused transmission
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In the blind, visual cortex is reassigned to audition and touch.
Therefore, we chose not to statically model brain areas! Brain areas should emerge
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Motivation: Architectural Concepts
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Symbolic Value Systems Sutton & Barto 1981:
reward as positive values Delayed rewards
Ogmen 1997: Punishments, rewards, novelty Sporns et al. 1999: Darwin robot Kakade & Dayan 2002: novelty and shaping Oudeyer et al. 2007: error max; progress max; similarity-
based progress maximization Huang & Weng 2007: punishment, reward, novelty Cox & Krichmar 2009: Neuromodulation as a robot controller Singh et al. 2010: reward and evolution
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Symbolic: Q-Learning
Symbolic reinforcement learning No need for state transition probability
Value driven Does not allow state inconsistency
Future value estimation for delayed rewards Time discount model: state-action value:
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Q-Learning Limitations
Symbolic: Exponential number of states, static Brittle
Fixed, greedy value function: Immediate rewards are preferred
Does not allow perception Does not allow creativity for other non-
modeled concepts
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Theory: For Any AFA There Is a GDN
Weng IJCNN 2010
GDN: GenerativeDevelopmental Network
AFA: Agent Finite Automaton
Marvin Minsky criticized ANNs
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Emergent: 5-HT & DA Systems Blue: 5-HT system Red: DA system Mechanism 1 (extra cell):
Mechanism 2 (within cell):zip increases the threshold Tzis reduces the threshold TIf pre-action > T, fire
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5-HT, DA, Ach, NE
Serotonin (5-HT): pain, stress, threats and punishment
Dopamine: (DA)pleasure, wanting, anticipation, and reward
Acetylcholine (Ach): expected uncertainty?
Norepinephrine (NE): novelty (unexpected uncertainty)?
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Emergent Value Systems
Daly, Brown, Weng 2011, Paslaski, VanDam, & Weng 2011, Weng et al. Neural Networks 2013: Neuromorphic Motivated Systems based on DN: 5-HT and DA Wandering or foraging, face recognition
Wang, Wu, & Weng 2011, 2012 Neuromorphic Motivated Systems based on DN: Ach and NE Novelty and Uncertainty Synapse maintenance Segmentation of objects from backgrounds
Zheng, Qian, Weng, Zheng 2013 Effects on internal brain areas: change learning rates
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Effects of 5-HT and DA on Y?
Effect on Z area: Inhibit and excite actionsWeng et al. IJCNN 2011
Effect on Y area:on learning rateZheng, Qian, Weng & Zhang, IJCNN 2013
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Dopamine System
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Serotonin System
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Acetylcholine System
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Norepinephrine System
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Bottom-Up Weights and 5-HT
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Y Weights for Z Neurons
Paslaski et al. IJCNN 2011
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Error Rates for Recognition
Paslaski et al. IJCNN 2011
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Navigation, Wandering, Foraging
Three synthetic agents: Self (S) Attractor (A) Repulsor (R)
5-HT: distance-lonely and distance-fear
DA: d < distance-desire
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Setting
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Environmental Settings
No brainer (control): no pain, no pleasure Love or War (D-lonely, D-fear) = (50, 50) Little Danger (D-lonely, D-fear) = (25, 125) Much Danger (D-lonely, D-fear) = (125, 25) Looking over the Fence
(D-lonely, D-fear) = (125, 125)
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Average Distance
Daly et al. IJCNN 2011
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Ach and NE: Goal for Segmentation
Wang, Wu and Weng, IJCNN 2011
(a) Bottom-up input to a neuron. (b) True object contour. (c) Estimated synaptogenic factor
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Global Confidence: Face Recognition
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Ach & NE: Receiver Operating Curve
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ACh Estimates
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DN: Motivational System as Emotion
Emergent nervous system Modeled:
5-HT (serotonin) DA (dopamine) Ach (acetylcholine) NE (norepinephrine)
Basic and higher emotion: developed from experience